Diversity in recommender systems has drawn attention in recent years [1]}.
Diversity can be defined as the potential of recommender system algorithms to recommend different or diverse content, e.g., recommending less popular items and targeting more niche items, while making personalized recommendation to users.
In [2]}, the authors provide an overview of
different definitions and measurements for diversity.
Here, we are interested in the impact of side information on the diversification of the recommendation output.
Diversification is important for recommendations to be useful to the user.
Its importance is reflected in a surge of recent work on improving diversity, such as [3]}, a multi-attribute diversification method, and [4]}, attribute-aware diversifying sequential recommender (SR).
Other work on diversity has focused on enhancing the user experience with system, such as [5]}, which showed the importance of diversity.
In this paper, our focus is measuring diversity rather than attempting to improve it.
